Preliminary communication

https://doi.org/10.31341/jios.46.2.2

Office Documents Classification under Limited Sample. A Case of Table Detection Inside Court Files

Paweł Baranowski orcid.org/0000-0002-4860-3578 ; Department of Economics and Sociology, University of Lodz, Lodz, Poland, and Section of Economic Cybercime, Institute of Economic and Financial Research, Lodz, Poland
Adrian Stepniak ; Department of Economics and Sociology, University of Lodz, Lodz, Poland

Abstract

Deep convolutional neural networks (CNNs) became an industry standard in image processing. However, in order to keep their high efficiency, a large annotated sample is required in the case of supervised learning. In this paper we apply the techniques specific for relatively small sample to a court files dataset. Specifically, we propose transfer learning and semisupervised learning to classify scanned page as having a table or not. We use four CNNs architectures established in the literature and find that transfer learning improves the classification performance, compared to the fully supervised learning. This result is especially evident in the scenarios where only a part of convolutioanl layers are transferred. The gains from semisupervised learning are ambiguous, as the results vary over CNNs architectures. Overall, our results show that office documents classification can achieve high accuracy when transferring initial convolutional layers is applied.

Keywords

Convolutions; Deep learning; Document processing; Image classification; Office documents

Hrčak ID:

290995

URI

https://hrcak.srce.hr/290995

Publication date:

22.12.2022.

Visits: 482 *